Lateral Deformation Prediction of Deep Foundation Retaining Structures Based on Artificial Neural Network

doi:10.16183/j.cnki.jsjtu.2023.109

Abstract

Abstract:

In order to more accurately predict the lateral deformation of retaining structures caused by foundation pit excavation, this paper adopts support the vector machine model, traditional artificial neural network model, and two kinds of recurrent neural network models considering temporal inputs to establish a prediction model for the maximum lateral deformation of retaining structures in different foundation pits, and for the same foundation pit under different working conditions. The results show that the artificial neural network can update and predict the deformation of the retaining structure in real time based on the measured data of the project, which is helpful for timely planning of the next construction process of the project. In the prediction of lateral deformation of retaining structures under different working conditions, the cyclic neural network model considering temporal inputs is better than the traditional artificial neural network model.

Key words: excavation, deformation prediction, machine learning, retaining structures

CLC Number:

TU46+3

XU Changjie, LI Xinyu. Lateral Deformation Prediction of Deep Foundation Retaining Structures Based on Artificial Neural Network[J]. Journal of Shanghai Jiao Tong University, 2024, 58(11): 1735-1744.

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编号	X₁/m	X₂	X₃/kN	X₄/kN	X₅/kN	X₆/kN	X₇/kN	X₈/d	X₉/(mm·h^-1)	Y/mm
1	5	1	1 913.59	0	0	0	0	22	-0.22	-0.66
2	5	1	2 369.11	0	0	0	0	25	0.43	0.63
3	5	1	2 309.13	0	0	0	0	27	0.37	1.37
︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙
78	26.05	7	1 870.24	7 096.59	968.79	845.28	1 107.62	118	1.54	58.14
79	26.05	7	1 677.10	7 336.54	925.58	791.38	1 100.69	119	-1.55	56.59
80	26.05	7	2 028.95	7 431.26	978.13	870.61	1 152.58	120	-0.4	56.19

独热编码前的参数	独热编码后的参数	对应的施工阶段
1	1000000	h≤5 m
2	0100000	5 m<h≤10 m
3	0010000	10 m<h≤15 m
4	0001000	15 m<h<26.05 m
5	0000100	h=26.05 m
6	0000010	架设第6道支撑
7	0000001	架设第7、8道支撑

模型	隐藏层层数	神经元数量	步长	学习率	激活函数	训练步数
MLP	3	64	-	0.000 1	ReLU	4 500
LSTM	1	64	4	0.000 1	ReLU	13 000
					Sigmoid	13 000
					tanh	13 000
GRU	1	64	6	0.000 5	ReLU	8 000
					Sigmoid	8 000
					tanh	8 000

模型	E_MSE	R²
SVM	4.99	0.99
MLP	36.96	0.95

模型	E_MSE	R²	预测用时/min
MLP	1.01	0.96	1.7
LSTM	0.77	0.97	4.7
GRU	0.75	0.97	4.3